1 / 17

Characterizing activity in AGN with X-ray variability

Characterizing activity in AGN with X-ray variability. Rick Edelson. Snippets of history. Optical discovery & study came first Seyfert classification based on emission lines First observations only possible in optical Still most accessible, well-studied waveband

cutler
Télécharger la présentation

Characterizing activity in AGN with X-ray variability

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Characterizing activity in AGNwith X-ray variability Rick Edelson

  2. Snippets of history • Optical discovery & study came first • Seyfert classification based on emission lines • First observations only possible in optical • Still most accessible, well-studied waveband • Flat IR thru X-ray SEDs, e.g. Elvis (1987) • Mushotzky (2004, astro/ph0405144) review • Concl: most effective AGN surveys in X-rays • Essentially all “Radiating Supermassive Black Holes” (AGN) show detectable hard X-ray activity

  3. X-rays are best activity indicator: 1) Reach deepest into heart of the AGN • Rapid var → emission from inner lt-hrs • Natural probe of central engine 2) No confusing emission components • Other local components and external sources generally don’t emit strongly in X-rays • Other ls provide info on orientation, etc. • Produced lt-days to lt-years out

  4. Principal Component Analysis • “PCA” first applied to AGN by Boroson & Green (1992, ApJS, 80,109) • Optical data on 92 opt/UV-selected quasars • “Principal Eigenvector”: strong correlation of Hb width and Fe II strength, other line params • Secondary strongly correlated with luminosity • Principal eigenvector linked to X-ray slope • Boller, Brandt & Fink (1996, A&A, 305, 53) • X-ray softness correlated with Hb width

  5. Boller et al. (1996) correlation of Hb FWHM and X-ray G

  6. X-ray variability in Radiating Supermassive Black Holes • Non-statistical indications of “extreme” variability in X-ray soft sources • IRS 13224: Boller et al. (1997, MN, 289, 393) • Akn 564: Edelson et al. (2002, ApJ, 568, 610) • Statistical link w/X-ray var. amplitude (sxs) • Turner et al. (1999, ApJ, 524, 667) andO’Neill et al. (2005, MNRAS, 358, 1405) • Correlated “excess variance” w/ various properties for ~day-long ASCA light curves • Found corr. w/ luminosity, optical params.

  7. 35 days of X-ray coverage of Akn 564. Note strong X-ray variability; UV/optical varied 15% peak-peak in this period.

  8. Sixteen single-orbit light curves (1 point on previous graph) in which Akn 564 varies by factor of 2 within 3000 sec.

  9. Why X-ray Varibility Classification? • AGN “stick out” the most in the X-rays • X-rays give best access to nuclear region • Bulk of lower-energy from lt-weeks–years out • Optical emission lines formed lt-days out • X-rays come from inner lt-hours • Variability indicates activity time/size scale • Test this by correlating X-ray variability with traditional eigenvectors of activity

  10. XMM and X-ray variability • Rapid X-ray variability is a powerful tracer of activity in Radiating SMBHs • XMM provides best opportunity to exploit it • LEO light curves (ASCA, Swift) are interrupted this destroys key info on 3-10 ks timescale • XMM can detect var. on <100 sec timescales • Chandra also uninterrupted, but lower sens. • Sensitive, uninterrupted XMM light curves ideal probes of critical short timescales

  11. XMM Variability Study • w/ Simon Vaughan, Ken Pounds • XMM Variability Sample • 29 Sy1s w/ >30 ks obs, good bkgd, opt. data • Measured Excess Variance (sxs) • Measured 4 ks time scale: shortest ever • Errors on individual estimate of order unity • Averaged multiple (10-100) estimates to beat down errors • Confirmed that sxs stable in different periods

  12. XMM light curves of sources w/ a range of variability levels. Note the tabulated quantity is Fvar = sqrt(sxs2). Fvar = 41% Fvar = 22% Fvar < 1.7% Fvar < 1.7% Fvar = 19% Fvar = 11%

  13. Variability Study Results • Used ASURV to correlate 4 parameters: • X-ray excess variance (sxs) • X-ray slope (G) • Hb FWHM • Luminosity (0.2-10 keV) • Strongest correlations involved Hb • sxs vs. Hb FWHM (p < 0.01%) • G vs. Hb FWHM (p = 0.26%) • sxs vs. Lx weaker than expected (p = 1.6%)

  14. Multi-parameter correlations. The strongest correlations are shown on the left. p < 0.01% p = 0.52% p = 1.6% p = 0.26% p = 6.7% p = 22%

  15. Implications • Short time scale X-ray variability better correlated w/ Hb FWHM than luminosity • X-ray variability most likely linked to mass of supermassive black hole → Hb FWHM is a better mass indicator than luminosity → Efficiency is not constant • Improved X-ray, optical data; censored PCA methods key to further progress

  16. State of X-ray Astronomy • Right now lots of X-ray satellites: XMM, Chandra, RXTE, Suzuki & Swift • Con-X, XEUS mega-missions planned for the 2020s • Doubtful they will proceed fully as hoped • No missions are planned for the interim • We will lose the ability to see in the X-rays starting in about 10 years • This would be a disaster for AGN studies

  17. Conclusions • Rapid X-ray variability most strongly correlated with Hb FWHM (an indicator of SMBH mass) • X-rays are allowing the deepest probes of the central environment • Access to the X-rays will be lost in next ~10 years unless we act quickly

More Related